雷达学报2019，Vol.8Issue(3)：413-424,12.
基于级联卷积神经网络的大场景遥感图像舰船目标快速检测方法
Fast Detection of Ship Targets for Large-scale Remote Sensing Image Based on a Cascade Convolutional Neural Network
摘要
Abstract
For the fast detection of ships in large-scale remote sensing images, a cascade convolutional neural network is proposed, which is a cascade combination of two Fully Convolutional Neural networks (FCNs), the target FCN?for Prescreening (P-FCN), and the target FCN?for?Detection (D-FCN). The P-FCN is a lightweight image classification network that is responsible for the rapid pre-screening of possible ship areas in large-scale images. The region proposals generated by the P-FCN have less redundancy, which can reduce the computational burden of the D-FCN. The D-FCN is an improved U-Net that can accurately detect arbitrary-oriented ships by adding target masks and ship orientation estimation layers to the traditional U-Net structure for multitask learning. In our experiment, TerraSAR-X remote sensing images and the optical remote sensing images obtained from the 91 satellite map software and the DOTA dataset were used to test the network. The results show that the detection accuracy of our method was 0.928 and 0.926 for synthetic aperture radar images and optical images, respectively, which were close to the performance of the traditional sliding window method. However, the running time of the proposed method was only about 1/3 of that of the sliding window method. Therefore, the cascade convolutional neural network can significantly improve the target detection efficiency while maintaining the detection accuracy and can realize the rapid detection of ship targets in large-scale remote sensing images.关键词
舰船目标检测/ 深度学习/ 全卷积网络/ 大场景遥感图像/ 快速检测/Key words
Ship detection/ Deep learning/ Fully Convolutional Neural network(FCN)/ Large scale remote sensing image/ Fast detection/分类
信息技术与安全科学引用本文复制引用
陈慧元,刘泽宇,郭炜炜,张增辉,郁文贤..基于级联卷积神经网络的大场景遥感图像舰船目标快速检测方法[J].雷达学报,2019,8(3):413-424,12.基金项目
国家自然科学基金(61331015, U1830103) （61331015, U1830103）
